Abstract
A new approach is proposed for extraction of features from human preferences reasoning. Conditional Probability Table (CPT) is a mentality representation to control the reasoning in Bayesian Belief Network (BBN). A software tool was developed using texture analysis with a co-occurrences matrix algorithm. As a case study, it was tested on BBN of moss (Rhacomitrium canescens) produce preferences. The result successfully represented features extracted as specific patterns. It is applicable as a new computational method for reducing many concrete parameters (Dimensionality) and extracting the information from CPT in five textural features. These features are essential as abstractive parameters for designing customized agro-industrial production to provide every consumer with a produce that matches his or her unique preferences.
